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assume agent's reasoning capabilities (in the sense of information processing),
but rarely this feature is explicitly modeled. Agents are pushed or pulled, with
some degree of resistance, but such a representation of influence has already
been challenged [22]. We hypothesize that BDI frameworks have not encoun-
tered a wide diffusion among social scientists because most BDI architectures
are complex to use by non-computer-scientists. It is no coincidence that cogni-
tive, AI and computer scientists use this approach instead of social scientists.
On the other hand, cognitive and computer scientists do not implement agents
that interact socially to any significant extent in simulations.
This paper aims at evaluating a new framework for agent-based modeling,
which may be appealing for both streams of research in social simulation: it
explicitly models agents reasoning capabilities
and
it can be applied to socially
embedded and interacting agents. The approach we propose is built on well es-
tablished theories from social, cognitive, and computer science: the “strength
of weak ties” by Granovetter [18], the “argumentative nature of reasoning”
by Mercier & Sperber [21] and “computational abstract argumentation” by
Dung [9]. Computational abstract argumentation is a reasoning approach that
formalizes arguments and their relations by means of networks, where arguments
are nodes and attacks between arguments are directed links between such nodes.
We believe that this formalization, while it offers a logical and computational
machinery for agent reasoning, it is nevertheless friendly to social scientists, who
are already familiar with network concepts. There is already a plea for the use
of logic-related approaches in ABM [25], but we are not aware of any previous
ABM that uses argumentation to investigate social phenomena. Our approach
represents a framework in the sense that it leaves the modeler many degrees
of freedom: different embedding structures can be accommodated, as well as
different trust models and different ways of processing information. The only
pivotal point is the representation of information and reasoning with abstract
argumentation.
The paper proceeds as follows: in the next section, we discuss the concept of
embeddedness [18] that will be used to connect our agents in a relational context;
we then present a brief formalization of how agent reasoning and interacting
capabilities unfold; we present an implementation of this idea by means of an
ABM, along with its scheduling and discuss some experimental results; finally,
we conclude and present some ideas for future work.
2 Weak Ties and Social Agents
In social simulations, embeddedness is almost always represented with (more or
less explicit) network structures. Embeddedness could be something abstract, i.e.
represented with relational networks, or spatial, i.e. represented with Von Neu-
man or Moore neighborhoods. In any case, these different kinds of embeddedness
may all be explicitly represented by network topologies.
The basic idea of this social trait comes from Granovetter's hypothesis, which
states that our acquaintances are less likely to be connected with each other
